1. Technical Field
Example embodiments of the present invention relate in general to an object recognition technology in computer vision, and more specifically, to a method and device for recognizing an object from an input image using a designed descriptor based on a hierarchical structured pattern.
Example embodiments of the present invention also relate in general to a technique for analyzing a feature point of an input image for the recognition, and more specifically, to a technique in which a patch region for a feature point is hierarchically divided to generate a descriptor.
2. Related Art
Computer vision is a recently emerging field of research in computer science in which parts corresponding to vision in machines are studied. In a wider category, computer vision is one branch of artificial intelligence (AI) and focuses on using computers to realize general human visual recognition abilities.
Examples of a technical application that needs the computer vision include digital image processing, machine learning, pattern recognition, and object recognition. The object recognition refers to a technique for watching an image of an object and then discovering space information such as the type, size, direction, and position of the object in real time based on knowledge information that is previously learned. This is a challenge of the entire computer science field in addition to a robot field.
Technical needs on a technique for generating a feature point and a technique for generating a descriptor, each of which is an object representation scheme for object recognition in the computer vision field, increase in many applications such as object recognition, object retrieval, an intelligent monitoring system, an intelligent robot, virtual reality, and so on. Furthermore, the demand on a descriptor having of a low memory capacity and a high recognition performance has significantly increased with the increase in the mobile market. In order to represent such a high-efficiency object, a descriptor should have a recognition performance that is robust to various changes such as hiding, lighting, and rotation of an object and may also be processed in real time.
In the existing research, a scale-invariant feature transform (SIFT) and a seeded up robust features (SURF) are proposed and utilized as an algorithm for recognizing and representing an object. However, since its operation is complicated, it is difficult to perform processing in real time. Recently, after binary robust independent elementary features (BRIEF), a binary descriptor such as the Oriented FAST and Rotated BRIEF (ORB), Binary Robust Invariant Scalable Key points (BRISK), and Fast Retina Key point (FREAK) has been proposed, which shows significant enhancement in terms of an object recognition performance and speed of a descriptor. However, the ORB and BRISK have typical limitations in representing an object due to selection of a feature with learning or at random, and the FREAK enables high speed processing, but its recognition performance has not been demonstrated clearly.